12,119 research outputs found

    Relationship between personality and academic motivation in education degrees students

    Get PDF
    p. 327-341The present study aims to understand the relationship between the big five factors of personality and academic motivation. In addition, the following variables are taken into consideration; sex, age and type of educational studies. A quantitative methodology is used, in base to a not experimental, correlational study. The sample is composed of 514 students of the Faculty of Education of Leon’s University, between the three education degrees. To gather the information, participants were asked to complete the Learning and Motivation Strategies Questionnaire (CEAM) and the Personality Questionnaire Five Factor Inventory (NEO-FFI). The results show the significant relationship between personality facets and motivation variables. It should be noted that female results were higher in the values of intrinsic motivation, motivation towards teamwork, neuroticism, and kindness, and the male results were higher in self-efficacy. Additionally, it was observed that intrinsic motivation decreases progressively from the first to the fourth year of the degree, the need for recognition decreases in the two last study years, and the openness to experiences is higher in the last year of the degree. Finally, Social Education students are those that show a higher intrinsic motivation, self-efficacy, total motivation, openness to experiences, and neuroticism, while Primary Education students’ results were higher in the need for recognition.S

    Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets.

    Get PDF
    Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset

    Victims' Access to Justice in Trinidad and Tobago: An exploratory study of experiences and challenges of accessing criminal justice in a post-colonial society

    Get PDF
    This thesis investigates victims' access to justice in Trinidad and Tobago, using their own narratives. It seeks to capture how their experiences affected their identities as victims and citizens, alongside their perceptions of legitimacy regarding the criminal justice system. While there have been some reforms in the administration of criminal justice in Trinidad and Tobago, such reforms have not focused on victims' accessibility to the justice system. Using grounded theory methodology, qualitative data was collected through 31 in-depth interviews with victims and victim advocates. The analysis found that victims experienced interpersonal, structural, and systemic barriers at varying levels throughout the criminal justice system, which manifested as institutionalized secondary victimization, silencing and inequality. This thesis argues that such experiences not only served to appropriate conflict but demonstrates that access is often given in a very narrow sense. Furthermore, it shows a failure to encompass access to justice as appropriated conflicts are left to stagnate in the system as there is often very little resolution. Adopting a postcolonial lens to analyse victims' experiences, the analysis identified othering practices that served to institutionalize the vulnerability and powerlessness associated with victim identities. Here, it is argued that these othering practices also affected the rights consciousness of victims, delegitimating their identities as citizens. Moreover, as a result of their experiences, victims had mixed perceptions of the justice system. It is argued that while the system is a legitimate authority victims' endorsement of the system is questionable, therefore victims' experiences suggest that there is a reinforcement of the system's legal hegemony. The findings suggest that within the legal system of Trinidad and Tobago, legacies of colonialism shape the postcolonial present as the psychology and inequalities of the past are present in the interactions and processes of justice. These findings are relevant for policymakers in Trinidad and Tobago and other regions. From this study it is recognized that, to improve access to justice for victims, there needs to be a move towards victim empowerment that promotes resilience and enhances social capital. Going forward it is noted that there is a need for further research

    Network Slicing for Industrial IoT and Industrial Wireless Sensor Network: Deep Federated Learning Approach and Its Implementation Challenges

    Get PDF
    5G networks are envisioned to support heterogeneous Industrial IoT (IIoT) and Industrial Wireless Sensor Network (IWSN) applications with a multitude Quality of Service (QoS) requirements. Network slicing is being recognized as a beacon technology that enables multi-service IIoT networks. Motivated by the growing computational capacity of the IIoT and the challenges of meeting QoS, federated reinforcement learning (RL) has become a propitious technique that gives out data collection and computation tasks to distributed network agents. This chapter discuss the new federated learning paradigm and then proposes a Deep Federated RL (DFRL) scheme to provide a federated network resource management for future IIoT networks. Toward this goal, the DFRL learns from Multi-Agent local models and provides them the ability to find optimal action decisions on LoRa parameters that satisfy QoS to IIoT virtual slice. Simulation results prove the effectiveness of the proposed framework compared to the early tools

    Walking with the Earth: Intercultural Perspectives on Ethics of Ecological Caring

    Get PDF
    It is commonly believed that considering nature different from us, human beings (qua rational, cultural, religious and social actors), is detrimental to our engagement for the preservation of nature. An obvious example is animal rights, a deep concern for all living beings, including non-human living creatures, which is understandable only if we approach nature, without fearing it, as something which should remain outside of our true home. “Walking with the earth” aims at questioning any similar preconceptions in the wide sense, including allegoric-poetic contributions. We invited 14 authors from 4 continents to express all sorts of ways of saying why caring is so important, why togetherness, being-with each others, as a spiritual but also embodied ethics is important in a divided world

    In her own words: exploring the subjectivity of Freud’s ‘teacher’ Anna von Lieben

    Get PDF
    This project is inspired by Roy Porter (1985), who draws attention to the patient-shaped gap in medical history, and Rita Charon (2006), who emphasises the need to bring the patient’s narrative to the fore in the practice of medicine. The principal aim was to devise a means of accessing the lived experience of a patient who is no longer alive in order to gain an understanding of her narrative. Anna von Lieben was identified as a suitable subject as she wrote a substantial quantity of autopathographical poetry suitable for analysis and her status as Freud’s patient makes her a person of significant interest to the history of medicine. The poems were analysed using Interpretative Phenomenological Analysis (IPA), an idiographic and inductive method of qualitative research, based on Heideggerian hermeneutic phenomenology, which explores the lived experience of individuals and is committed to understanding the first-person perspective from the third-person position. The main findings from the IPA study reveal that Anna experienced a prolonged period of malaise, starting in late adolescence which she believed to result, at least partly, from a traumatic experience which occurred at that time. The analysis also indicates that Anna suffered from deep and lasting feelings of guilt and shame. The discovery of additional family documentation enabled me to contextualise and add substance to the findings of the IPA study. Anna’s husband’s diaries in particular reveal that Anna: ‱ had a severe and longstanding gynaecological disorder ‱ suffered from severe morphinism ‱ did not benefit from Freud’s treatment which seemed neither to ease her symptoms nor identify any cause ‱ was treated in Paris, not by Jean-Martin Charcot as previously supposed, but by a French hydrotherapist, Theodore Keller, who appears to have become a person of considerable significance in her life. The above findings led me to investigate Anna’s comorbidities (gynaecological disease and morphinism) and to show how those could be responsible for much of the symptomatology identified by Freud as ‘hysteria’. I then explore the possibility that her psychotic-like experiences could have been iatrogenically induced by her treatment first by Keller and then by Freud. Finally, I propose a fourfold set of hypotheses as an alternative to Freud’s diagnosis of hysteria

    How to Be a God

    Get PDF
    When it comes to questions concerning the nature of Reality, Philosophers and Theologians have the answers. Philosophers have the answers that can’t be proven right. Theologians have the answers that can’t be proven wrong. Today’s designers of Massively-Multiplayer Online Role-Playing Games create realities for a living. They can’t spend centuries mulling over the issues: they have to face them head-on. Their practical experiences can indicate which theoretical proposals actually work in practice. That’s today’s designers. Tomorrow’s will have a whole new set of questions to answer. The designers of virtual worlds are the literal gods of those realities. Suppose Artificial Intelligence comes through and allows us to create non-player characters as smart as us. What are our responsibilities as gods? How should we, as gods, conduct ourselves? How should we be gods

    Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting

    Get PDF
    Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models' performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the Stacked-LSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications
    • 

    corecore